The growing complexity of transportation networks and the increasing frequency of security threats have necessitated the development of intelligent, responsive monitoring systems. This paper explores the convergence of Integrated Geographic Information Systems (GIS) and Artificial Intelligence (AI) surveillance technologies to enhance real-time transportation threat detection and response. Advances in spatial data analysis, geospatial mapping, machine learning, and computer vision are revolutionizing the way transportation systems are monitored, enabling the identification of anomalies, prediction of potential risks, and optimization of emergency responses. By integrating real-time geospatial data with AI-powered surveillance tools—such as intelligent cameras, unmanned aerial vehicles (UAVs), and sensor networks—authorities can achieve a dynamic, location-aware understanding of threats. The study also discusses the role of data fusion, edge computing, and deep learning in improving the speed, accuracy, and scalability of such systems. Furthermore, it addresses challenges related to data privacy, system interoperability, and infrastructure costs. This synthesis highlights how the fusion of GIS and AI surveillance can significantly strengthen transportation security frameworks and support smart city initiatives through proactive, adaptive threat monitoring.